class _NormBase(Module): #源码
"""Common base of _InstanceNorm and _BatchNorm"""
_version = 2
__constants__ = ['track_running_stats', 'momentum', 'eps', 'num_features', 'affine']
def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True, track_running_stats=True):
super(_NormBase, self).__init__()
self.num_features = num_features
self.eps = eps
self.momentum = momentum
self.affine = affine
self.track_running_stats = track_running_stats
if self.affine:
self.weight = Parameter(torch.Tensor(num_features))
self.bias = Parameter(torch.Tensor(num_features))
else:
self.register_parameter('weight', None)
self.register_parameter('bias', None)
if self.track_running_stats:
self.register_buffer('running_mean', torch.zeros(num_features))
self.register_buffer('running_var', torch.ones(num_features))
self.register_buffer('num_batches_tracked', torch.tensor(0, dtype=torch.long))
else:
self.register_parameter('running_mean', None)
self.register_parameter('running_var', None)
self.register_parameter('num_batches_tracked', None)
self.reset_parameters()
torch.nn.BatchNorm1d(num_features, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
#详细见https://blog.csdn.net/LoseInVain/article/details/86476010
